arXiv AI
Cognitive Admission Control: Risk-Conditioned Assurance for Consequential Actions in Agentic Distributed Systems
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arXiv AI
Oct 21
Runtime Control for AI Agents: Closing the Evaluation--Execution Gap in Operational Workflows
The paper introduces a runtime assurance layer for language‑model agents operating in operational workflows, addressing the evaluation–execution gap where approvals may become invalid after changes in facts, evidence, policy, or authority. The proposed state‑bound configuration separates probabilistic measurement from deterministic adjudication, binds execution allowances to the basis of an approval, invalidates them upon logged changes, and rechecks admissibility at dispatch. In a scripted 42‑case replay, the approach identified 15 later‑invalidated approvals that a non‑re‑evaluating comparator missed, holding 14 affected actions before dispatch and compensating one landed effect.
arXiv AI
Aug 24
AID-Guard: Stateful Authorization for Delegated Agent Effects
arXiv:2608.21159v1 Announce Type: cross Abstract: Tool-using AI agents turn delegated tasks into provider effects, yet authorization often ends at admission while provider state, delivery, retry, and...
arXiv AI
Sep 18
When AI Agents Commit: Cognitive Serializability Across Data, Evidence, Policy, and Authority
The paper introduces the concept of Cognitive Serializability for autonomous AI agents, ensuring that mutations derived from dynamic inputs—such as database reads, evidence, policy, beliefs, and delegated authority—are committed in a serial, logically consistent order. It presents a framework called Trusted Cognitive Transaction (TCT) that combines immutable executable definitions, sealed envelopes, guard-first commits, and receipt-driven reconciliation to enforce serializability and prevent anomalies. Experimental results show that the prototype implementation incurs minimal overhead while eliminating injected anomalies.
arXiv AI
Oct 28
CASEL: Consequence-Aware Selective Execution for Reliable Long-Horizon Tool-Using Agents
CASEL (Consequence-Aware Selective Execution Layer) is a runtime allocation policy for tool‑using language agents that chooses among direct execution, verification, sandboxing, clarification, approval, and abstention for each proposed tool call. It combines calibrated failure probability, state‑dependent consequence, reversibility, ambiguity, and evidence freshness to minimize a per‑mode cost. In controlled simulations, CASEL cuts harmful‑side‑effect trajectories from 14.75 % to 7.02 % and reduces mean normalized harm from 0.184 to 0.123 compared to an uncertainty‑ranked gate.
arXiv AI
Oct 27
Responsibility Boundaries for Agentic Systems: A Comparative Evaluation of Probabilistic Agentic Programming
The paper introduces Probabilistic Agentic Programming (PAP), a framework that explicitly defines responsibility boundaries for language‑model agents by linking mandate, authority, effects, and evidence. It compares PAP to various programming paradigms and agent systems across seven dimensions, and presents a mediation kernel that enforces checks before tool execution, operation‑bound approval, and resource limits. An exploratory study with seven business cases and multiple models demonstrates that PAP’s native Touba implementation achieves the highest or tied observation counts, supporting its effectiveness in managing agentic behavior.
arXiv AI
Jun 9
Semantic Quorum Assurance: Collective Certification for Non-Deterministic AI Infrastructure
arXiv:2606. 08021v1 Announce Type: cross Abstract: As large language model (LLM) agents are integrated into autonomous cloud operations, distributed systems face a semantic reliability problem: proposer agents can generate production mutations, such as modifying IAM policies, opening firewall security groups, or executing data exports, that are syntactically valid and statically authorized but operationally unsafe.